Image enhancement and application functionality for medical and other uses

ABSTRACT

The disclosure herein provides beneficial systems, methods, devices, and apparatuses that enhance and/or analyze images, and that can be configured to provide users an assessment and/or recommendation based on the enhanced and/or analyzed images. In an embodiment related to medicine, the assessment and/or recommendation is based on a patient situation, dimensions of patient organs/lumens, or the like in order to achieve personalized medicine.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit under 35 U.S.C. §120 to U.S.Application Ser. No. 12/398,927, filed Mar. 5, 2009, now U.S. Pat. No.8,275,201, which claims the benefit under 35 U.S.C. §119(e) to U.S.Provisional Application No. 61/034,442, filed Mar. 26, 2008, which arehereby incorporated by reference in their entirety, includingspecifically but not limited to the devices, systems, and methodsrelating to image enhancement of devices, and the applications of suchtechniques disclosed therein.

BACKGROUND

1. Field

Embodiments of the invention relate to the general field of imageenhancement, and, in particular, to methods, systems, and devices forapplying image enhancement techniques for viewing medical/treatmentdevices in situ and/or other devices to determine the status, condition,delivery, positioning, deterioration, usefulness, and the like of suchdevices.

2. Description of the Related Art

With the development of new technologies, devices can be inserted intopatients, buildings, or other areas and/or places wherein it isdifficult to visualize the device after it has been implanted orinstalled. For example, it is difficult to visualize a stent or othermedical/treatment device after it has been implanted into anartery/vessel/lumen or other area of the body without having toperforming an invasive surgery. The same is generally true for any otherdevices installed into an area that is hidden from view. In certaincircumstances, it may be possible to view such devices using an imagingapparatus employing an imaging modality, such as X-ray, magneticresonance imaging (MRI), sonar, ultrasound, or other like. The images,however, produced from such imaging apparatuses can be of poor qualitywherein visualization of the data can be difficult such that the usercannot make a decision about the device.

SUMMARY

Embodiments of the invention described herein are directed to imageenhancement for medical and other uses. Other embodiments are directedto a computer-based evaluation of a medical or other image.

In an embodiment, a computer-implemented method for evaluating a stentin situ is provided, the computer-implemented method comprisingaccessing a computer accessible database to obtain at least one medicalimage; selecting a region of interest in the at least one medical image,wherein the region of interest comprises the stent in situ, wherein thestent comprises a plurality of cells; cropping the region of interestfrom the at least one medical image to generate a cropped image;identifying the background of the cropped image; subtracting pixels fromthe cropped image, wherein the subtracted pixels represent thebackground; normalizing a grayscale of the cropped image; adjustingcontrast of the cropped image to produce a first enhanced image; andoutputting the first enhanced image to the user.

The computer-implemented method can further comprise applying an unsharpmask filter to the enhanced image. In an embodiment, thecomputer-implemented method further comprises measuring intensity valuesof different pixels in the first enhanced image; and calculating aquantitative characteristic of the first enhanced image based on themeasured intensity values. In an embodiment, the method steps areperformed in the order listed above. The computer-implemented method canfurther comprise identifying a first vertex of a first cell in the stentwithin the first enhanced image; identifying a second vertex of thefirst cell in the stent within the first enhanced image; determining adistance between the first and the second vertexes, wherein the distanceis correlated to stent elongation; accessing a computer accessible indexto determine whether the distance exceeds a threshold value for stentelongation; and outputting a recommendation to the user related to thestent if the distance exceeds the threshold value.

In an embodiment, the computer-implemented method can further compriseidentifying an angle formed within a cell in the stent within the firstenhanced image; determining a number of degrees in the angle; accessinga computer accessible index to determine whether the number of degreesexceeds a threshold value for stent elongation; and outputting arecommendation to the user related to the stent if the number of degreesexceeds the threshold value. In an embodiment, the angle is formed by apeak or valley within the cell. In an embodiment, the outputting outputsa recommendation related to the stent if the angle formed by the peak orvalley is 29 degrees or less. In an embodiment, the outputting outputs arecommendation related to the stent if the angle formed by the peak orvalley is 21 degrees or less. In an embodiment, the angle is formed neara cell interconnection area and is within the cell. In an embodiment,the outputting outputs a recommendation related to the stent if theangle is 60 degrees or more. In an embodiment, the outputting outputs arecommendation related to the stent if the angle is 70 degrees or more.

The computer-implemented method can further comprise analyzing a lineprofile of the first enhanced image, wherein the line profile is takenacross the width of the stent; determining the metal coverage of theline profile; accessing a computer accessible index to determine whetherthe metal coverage is below a threshold value correlated to stentelongation; and outputting a recommendation to the user related to thestent if the metal coverage is below the threshold value. In anembodiment, the computer-implemented method further comprises accessingthe computer accessible database to obtain a second medical image,wherein the second medical image comprises an image of the stent in situat a different time; processing the second medical image to produce asecond enhanced medical image; determining one or more disparitiesbetween the second enhanced medical image and the first enhanced image;accessing a computer accessible index to determine whether the one ormore disparities exceed a threshold value; and outputting to the user arecommendation related to the stent if the one or more disparitiesexceed the threshold value.

The computer-implemented method can further comprise determining in thefirst medical image a first distance between a first vertex of a firstcell in the stent and a second vertex of the first cell in the stent;determining in the second medical image a second distance between thefirst vertex of the first cell in the stent and the second vertex of thefirst cell in the stent; wherein the one or more disparities is based ona difference in the first distance and the second distance. In anembodiment, the computer-implemented method further comprisesdetermining in the first medical image a first number of degrees in afirst angle between a first cell in the stent; determining in the secondmedical image a second number of degrees in a second angle the firstcell; wherein the one or more disparities is based on a difference thefirst angle and the second angle.

In an embodiment, the computer-implemented method can further comprisedetermining in the first medical image a first metal coverage in a lineprofile; determining in the second medical image a second metal coveragein the line profile; wherein the one or more disparities is based on adifference in the first metal coverage and the second metal coverage.

In another embodiment, a computer-implemented method for evaluating amedical image, comprises obtaining a medical image of a desired locationwithin a patient; measuring pixel intensity at one or more regions ofinterest of the medical image; and calculating a quantitativecharacteristic of the desired location based on the measured pixelintensity. In an embodiment, the computer-implemented method furthercomprises comparing the quantitative characteristic with one or morepredetermined threshold values. In an embodiment, the medical image isof an implanted stent. In an embodiment, the quantitative characteristicis elongation of the stent. In an embodiment, the quantitativecharacteristic is radial compression of the stent. In an embodiment, themedical image is of a stenosed blood vessel. The computer-implementedmethod can further comprise outputting to a user a desired treatmentmodality based on the calculated quantitative characteristic.

In another embodiment, a system for enhancing a medical image comprisesan image processing module configured to select a region of interest inthe medical image, and to crop the region of interest from the medicalimage to generate a cropped image; an image background subtractionmodule configured to identify and subtract background pixels of thecropped image, and normalize a grayscale of the cropped image; acontrast adjustment module configured to adjust contrast of the croppedimage to generate a first enhanced image; an output module configured tooutput the first enhanced image to a user; and the system comprising aprocess and memory.

In another embodiment, a computer-implemented method for processing amedical image comprises accessing a computer accessible database toobtain at least one medical image; selecting a region of interest in theat least one medical image, wherein the region of interest comprises avessel; cropping the region of interest from the at least one medicalimage to generate a cropped image; identifying background pixels of thecropped image; subtracting the background pixels from the cropped image,wherein the subtracted pixels represent the background; normalizing agrayscale of the cropped image; adjusting contrast of the cropped imageto generate an enhanced image; and outputting the enhanced image to theuser.

In another embodiment, a system for generating an assessment report ofan image comprises a communications module for accessing a securenetwork connection between a remote image enhancement and analysissystem; a storage repository configured to store threshold valuesrelating to specific devices or conditions; a data manager moduleconfigured to receive data values from the remote image enhancement andanalysis system through the communications module, and to store the datavalues in the storage repository; a threshold reassessment moduleconfigured to recalculate the threshold values based on data valuesrelated to the threshold values and to store recalculated thresholdvalues in the storage repository; and an assessment module configured toreceive a request and measured values from the remote image enhancementand analysis system through the communications module, wherein therequest is to compare the measured values to related threshold values.

For purposes of this summary, certain aspects, advantages, and novelfeatures of the invention are described herein. It is to be understoodthat not necessarily all such advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves one advantage or groupof advantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. The foregoing and other features, aspects andadvantages of the present invention are described in detail below withreference to the drawings of various embodiments, which are intended toillustrate and not to limit the invention. The drawings comprise thefollowing figures in which:

FIG. 1 is a block diagram depicting a high level overview of oneembodiment of a system for image enhancement and analysis.

FIG. 2 is a block diagram depicting a high level overview of oneembodiment of a system flow for the image enhancement and analysissystem.

FIG. 3 depicts an example high-level flow diagram for an embodiment ofthe image enhancement and analysis system.

FIG. 4 depicts an example high-level flow diagram for an embodiment ofthe enhancement engine.

FIG. 5 depicts an example series of resulting images produced by oneembodiment of the image enhancement and analysis system.

FIG. 6A is depicts an example original medical image, and FIG. 6Bdepicts an example enhanced image resulting from one embodiment of theimage enhancement and analysis system.

FIG. 7A depicts an example original digital x-ray image, a correspondinghistogram of the original image, and a depiction of the original imageexpressed in digital format by pixel value.

FIG. 7B depicts an example cropped digital x-ray image, an examplecorresponding histogram of the cropped image, and an example depictionof the cropped image expressed in digital format by pixel value.

FIG. 8A depicts an example histogram of the cropped image in FIG. 7B.

FIG. 8B depicts an example histogram of the cropped image in FIG. 7Bwith the background pixels subtracted.

FIG. 9A depicts an example normalized image with the backgroundsubtracted, a corresponding histogram, and an example depiction of theimage expressed in digital format by pixel value.

FIG. 9B depicts an example image with contrast enhancement, acorresponding histogram, and an example depiction of the image expressedin digital format by pixel value.

FIG. 10 depicts example images of stent elongation.

FIG. 11 depicts example images of stenosis.

FIG. 12 depicts example images of calcification.

FIGS. 13A, 13B, and 13C are examples images depicting areas of stentradial compression and areas of stent elongation.

FIG. 14A depicts an example normal stent, and FIG. 14B depicts amagnified view of the example stent.

FIG. 14C is the example magnified view of the example stent highlightingexample cells of the stent.

FIG. 15 depicts example distances and angles in a magnified view of anexample stent.

FIG. 16A depicts an example normal stent, and FIG. 16C depicts acorresponding example single slice profile of the normal stent.

FIG. 16B depicts an example implanted stent, and FIG. 16D depicts acorresponding example single slice profile of the implanted stent.

FIG. 17A depicts an image of a stent having example regions of interest,and FIG. 17B depicts an example representation of plaque hardnessestimations for the example regions of interest depicted in FIG. 17A.

FIG. 18A is an example image depicting contrast flow before stenting.

FIG. 18B is an example image depicting contrast flow after stenting orduring atherectomy.

FIG. 19 is an example chart depicting flow dynamics of a vessel that isstented versus unstented.

FIG. 20A is an example chart depicting deployment rate of a stent, andFIG. 20B is a corresponding series of example images of a stentdeployment.

FIG. 21 is an example form used for generating and/or collectingclinical data.

FIG. 22 is a block diagram depicting one embodiment of a computerhardware system configured to run software for implementing one or moreembodiments of the image enhancement and analysis system describedherein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Although several embodiments, examples and illustrations are disclosedbelow, it will be understood by those of ordinary skill in the art thatthe invention described herein extends beyond the specifically disclosedembodiments, examples and illustrations and includes other uses of theinvention and obvious modifications and equivalents thereof. Embodimentsof the invention are described with reference to the accompanyingfigures, wherein like numerals refer to like elements throughout. Theterminology used in the description presented herein is not intended tobe interpreted in any limited or restrictive manner simply because it isbeing used in conjunction with a detailed description of certainspecific embodiments of the invention. In addition, embodiments of theinvention can comprise several novel features and no single feature issolely responsible for its desirable attributes or is essential topracticing the inventions herein described.

As used herein, the terms “stenosis,” “stricture,” and “coarctation” canbe interchangeably used, and generally and broadly refer to an abnormalnarrowing in a blood vessel and/or other lumen or tubular organ and/orstructure.

The terms “imaging device” or “imaging apparatus,” as they are usedherein, can be interchangeably used, and generally and broadly includewithout limitation any device employing any imaging modality, such asbut not limited to X-ray, magnetic resonance imaging (MRI), sonar,ultrasound, and other imaging techniques.

The terms “image,” and “medical image,” as used herein, can beinterchangeably used, and generally and broadly refer to any type ofimage or depiction of a device (for example, medical devices, pipes,vessels, wires, or other apparatuses), and may include withoutlimitation medical images, x-ray images, MRI images, sonar images,ultrasound images, OCT image, CAT scan, CT scan, PET scan, SPECT scan,fluoroscopy, static images, movie images, two dimensional images, threedimensional images, 8 bit images, 16 bit images, 32 bit images, 64 bitimages, or the like.

As used herein, the terms “user,” “physician,” “doctor,” are broadinterchangeable terms used to generally and broadly refer to any user ofthe systems, methods, devices described herein, and can include withoutlimitation a user, physician, doctor, technician, operator, nurse,professional, or the like.

The term “data repository” as used herein generally refers to anystorage medium located in any system, location, device or the like, andcan include without limitation a hard drive, a database, a removablemedium (for example, tape, disc, optical disc, thumb drive, RAM, ROM, orthe like), wherein each may be part of a device (for example, imagingdevice), system, computer, machine, appliance, equipment, or the like.

As used herein, the terms “radial compression,” “incomplete expansion,”and “residual stenosis” can be interchangeably used herein, and arebroad terms that generally refer to a reduction in the diameter of thestent, radial compression of the stent, incomplete expansion of thestent, residual stenosis, or the like.

In reviewing images obtained from an imaging device, users typicallyreview unenhanced (as-received) images using the naked eye in order tomake qualitative assessments of the image. Due to subtle differences inthe grayscale of the images, features of the image can be obfuscated,hidden, and/or blocked from the naked eye.

For example, in analyzing medical images, doctors typically review anunenhanced (as-received) medical image and make a qualitative assessmentof the image using the naked eye. This is done, for example, when adoctor or technician analyzes a medical image of an implanted stent toassess the stent (e.g., is the stent fractured, elongated, etc.). Due tothe subtleties of the grayscale medical image, it can be difficult for aphysician or other user to differentiate between the grayscales, and asa result, physicians or other users can sometimes inadvertentlymisdiagnose a patient or misinterpret a medical image. For instance, adoctor may review the medical image and determine that a stent isnormally implanted, when in fact it is actually fractured, but thedoctor was unable to see the fracture because the grayscale differentialof the medical image was too subtle.

Intensifying the contrast of the medical image alone may not rectifythis problem. The background can be still incorporated in the image andcan be intensified as well, thereby still not allowing visualization ofstent struts alone. As such, the subtleties of the grayscale image, evenin the intensified image, can still make it difficult to read thecontrast. Another issue that can be addressed in image enhancement isthat it is a visualization of a physical object and, as such, inenhancing the image, its integrity should be kept intact. Any imageenhancement that is performed on the original image should not distortthe “data” embedded within the image. If any distortion of the imagedata occurs, then the image can be considered compromised and can be ofdiminished value as a medical assessment and treatment tool.

Accordingly, there is a need for way to enhance images to make it easierto review while not compromising or distorting the underlying physicaldata embedded within the image.

Additionally, the review of images is highly subjective and qualitative,and depends largely on the user's experience and training. Therefore, itwould be beneficial for users to have a system that enhances andanalyzes images to provide the user an assessment and/or recommendationand/or alarm based on standardized and/or quantitative data. Using theenhanced image, the system could more accurately obtain measurements inthe image and/or other quantitative data from the image, and themeasurements could then be correlated to a database having thresholdvalues for various scenarios, devices, medical devices, environments,physicians, patients, techniques, surgical tools/equipment, or the like.For example, the database could be configured to store threshold valuesunique to a specific stent of a particular size, made from a definitematerial, by a certain manufacturer in a specific year. The thresholdvalues can be used to generate recommendations/alarms to users, such assuggested treatment modalities, or suggested treatment devices, or thelike, and such recommendations can be unique to the patient situation,condition, disease, or the like to achieve personalized medicine. Thethreshold values can be developed by reviewing and/or categorizingclinical data, clinical outcomes, device performance parameters, medicalsociety guidelines, and other similar research.

For example, the review of medical images by physicians is highlyqualitative and subjective and depends, to a great extent, on aphysician's experience and training. However, even where a physician ishighly trained, there may be inter-related variables that are not fullyappreciated by the physician that have a bearing on a proper diagnosis,variables such as stent architecture, compliance of the vessel,treatment modalities, device operation, vessel information, among othervariables. Accordingly, there is also a need for a system and methodthat can use an enhanced image to provide a physician with an assessmentof a patient's situation that takes into account a number ofinterrelated variables, such as those mentioned above, that are oftenbeyond the physician's expertise. Such an assessment system could beuseful in diagnostic/procedural/predictive environments before aprocedure or course of action; during a procedure as a feedback tool; oras a predictive tool or even post-procedure to quantify and evaluate theefficacy of the procedure or even as a predictive tool to assess thepotential long-term outcome of the procedure.

Accordingly, the disclosure herein provides beneficial systems, methods,devices, and apparatuses that enhance and/or analyze images, and thatcan be configured to provide users an assessment and/or recommendationbased on the enhanced and/or analyzed images. In an embodiment relatedto medicine, the assessment and/or recommendation is based on a patientsituation, dimensions of patient organs/lumens, or the like in order toachieve personalized medicine.

With reference to FIG. 1, there is illustrated a block diagram depictinga high level overview of one embodiment of an image enhancement andanalysis system 110. In the depicted embodiment, an image enhancementand analysis system 110 can be directly connected to an imagingapparatus 108, and can be connected through a network 130 to a pluralityof other imaging apparatuses 132, other enhancement and analysis systems134, other update/monitoring systems 136, and other master databases138.

In FIG. 1, the image enhancement and analysis system 110 is connected,directly or indirectly, to imaging apparatus 108 in order to receiveimages to be enhanced and analyzed. In other embodiments, the imageenhancement and analysis system 110 can be connected to and receiveimages from an image database 111, or the image enhancement and analysissystem 110 can be connected to and receive images through a network 130that is connected to remote image databases 131. Generally, images canbe generated by an imaging apparatus 108, 132. For example, an x-rayimaging apparatus can generate an image of a clogged pipe in a wall 102,a stent implanted in a patient, an atherectomy procedure 105, or inother situations 106 wherein a user requires imaging a device or areathat is hidden from view or not easily visualized with the naked eye.

As illustrated in FIG. 1, the image enhancement and analysis system 110can comprise a plurality of modules including but not limited to animage enhancement module 112, a pre-procedure analysis engine module114, an intra-procedure analysis engine module 116, and a post-procedureanalysis engine module 118. In certain embodiments, the pre-procedureanalysis engine module 114, the intra-procedure analysis engine module116, and the post-procedure analysis engine module 118 are connected todatabases 120, 122, 124 to retrieve and/or store data. For example, theimage enhancement and analysis system 110 can be configured to use imageenhancement module 112 to enhance the image received/obtained fromimaging apparatus 108, and the enhanced image can then be furtheranalyzed and/or processed using the pre-procedure analysis engine module114, the intra-procedure analysis engine module 116, and/or thepost-procedure analysis engine module 118. In analyzing and/orprocessing the enhanced image, the image enhancement module 112, thepre-procedure analysis engine module 114, the intra-procedure analysisengine module 116, and/or the post-procedure analysis engine module 118can be configured to generate a report, recommendation, feedback, or thelike based on threshold values and other data stored in the databases120, 122, 124. The enhanced image as well as the report, recommendation,feedback, or the like can be outputted on display device 126 and/orother output devices 128.

In reference to FIG. 1, the image enhancement and analysis system 110can be connected to an update/monitoring system 136 through network 130.In certain embodiments, the update/monitoring system 136 is configuredto monitor the image enhancement and analysis system 110, and/or theother image enhancement and analysis systems 134 to ensure that thesystems are working properly and accurately. The update/monitoringsystem 136 can be configured to update/add/delete/modify (on a batched,delayed, real-time, substantially real-time, and/or periodic basis) thesoftware, protocols, procedures, methods, and the like of the imageenhancement module 112, the pre-procedure analysis engine module 114,the intra-procedure analysis engine module 116, and/or thepost-procedure analysis engine module 118, the databases 120, 122, 124,and any other module and/or apparatus in or connected to the imageenhancement and analysis system 110.

With reference to FIG. 1, the update/monitoring system 136 can beconfigured to receive (on a batched, delayed, real-time, substantiallyreal-time, and/or periodic basis) data, reports, recommendations,feedback, or the like from image enhancement and analysis system 110,and other image enhancement and analysis systems 134 to store such datain master database 138. Such data can increase the amount of informationand/or feedback available for improving, altering, modifying thethreshold valves stored in the databases 120, 122, 124 and used by thepre-procedure analysis engine module 114, the intra-procedure analysisengine module 116, and/or the post-procedure analysis engine module 118.In certain embodiments, the update/monitoring system 136 is configuredto use such data to modify and/or improve (on a batched, delayed,real-time, substantially real-time, and/or periodic basis) the thresholdvalues and store such modified threshold values in the master database138. The update/monitoring system 136 can be configured to update (on abatched, delayed, real-time, substantially real-time, and/or periodicbasis) databases 120, 122, 124 with the modified threshold values storedin master database 138.

In reference to FIG. 1, the image enhancement and analysis system 110can be located at or near the site of the imaging apparatus 108, or theimage enhancement and analysis system 110, 134 can be located at aremote location from the imaging apparatus 108, 132. In certainembodiments, the image enhancement and analysis system 110, 134 isoperated as an application services provider (ASP) model wherein thecomputer image enhancement and analysis services are provided over asecure network 130 in order to offer users on-demand software services.

With reference to FIG. 1, the pre-procedure analysis engine module 114can be configured to analyze the enhanced image received/sent from theimage enhancement module 112 to output a recommendation on how toproceed before a procedure. In an embodiment related to medicine, therecommendation on how to proceed is based on the patient situation,dimensions of patient organs/lumens, or the like in order to achievepersonalized medicine. In the medical context, the pre-procedureanalysis engine module 114 can be, for example, configured to analyze amedical image of a vessel to output a recommendation as to anappropriate treatment modality (for example, angioplasty, stenting,etc.) and/or a suggested/recommended treatment device (for example, 5 mmstent) to be used. In certain embodiments, the intra-procedure analysisengine module 116 can be configured to analyze the enhanced imagereceived/sent from the image enhancement module 112 to output arecommendation based on statistical and/or clinical significance and/orother data on how to proceed during a procedure. In the medical context,the intra-procedure analysis engine module 116 can be, for example,configured to analyze a fluoroscopy medical image (or a plurality ofmedical images taken sequentially over time or taken at different times)of a stent being implanted into a vessel to output feedback (forexample, an appropriate speed for deploying the stent into the vessel)to the physician performing the surgery. The feedback can be based ondetected changes or disparities in measurements taken in the images,and/or whether the changes or disparities exceed or are below athreshold value. In certain embodiments, the post-procedure analysisengine module 118 can be configured to analyze the enhanced imagereceived/sent from the image enhancement module 112 to output arecommendation on what to do after a procedure. In the medical context,the post-procedure analysis engine module 118 can be, for example,configured to output a report as to the success of a procedure (forexample, stent properly deployed, proper device placement, predicteddevice durability) or to output a report as to the condition of aimplanted device (for example, stent elongated beyond threshold value,and/or likely to fracture).

As illustrated in FIG. 2, there is depicted an example high-levelprocess flow diagram of an embodiment of employing the image enhancementmodule 112, the pre-procedure analysis engine module 114, theintra-procedure analysis engine module 116, and the post-procedureanalysis engine module 118. Although this high-level process flowdiagram is in the medical context, one skilled in the art willunderstand that this high-level process flow diagram can be adapted toother contexts, such as in the plumbing context, welding industrycontext, oil exploration context, or the like.

With reference to FIG. 2, the image enhancement module 112 can beconfigured to receive/obtain an image at point 202 for a patientrequiring implantation of a treatment device 204. At block 206, theimage enhancement module 112 can be configured to locate the region ofinterest in the image automatically, semi-automatically, or the regionof interest can be located manually by the user. In certain embodiments,the region of interest can be focused on a particular portion of avessel such that the rest of the image can be cropped out of the image.At block 208, the image enhancement module 112 can be configured toperform image enhancement of the cropped image. In certain embodiments,the pre-procedure analysis engine module 114 can be configured at block210 to determine the length/distance of the object at issue, forexample, the diameter of an artery or vessel. In certain embodiments,the pre-procedure analysis engine module 114 can be configured at block214 to determine the condition of the object at issue, for example theseverity of stenosis of an artery or vessel. In certain embodiments, thepre-procedure analysis engine module 114 can be configured at block 216to determine the activity of the object at issue, for example the pulserate of an artery or vessel. In certain embodiments, the pre-procedureanalysis engine module 114 can be configured at block 212 to determineother factors, attributes, and/or characteristics of the object atissue, for example, the presence of previous medical intervention.

In reference to FIG. 2, the various factors, attributes,characteristics, and measurements obtained at blocks 210, 212, 214, 216are compared with stored threshold values at block 218. In certainembodiments, the threshold values are stored in threshold database 220or other database 120. Based on the foregoing comparison, thepre-procedure analysis engine module 114 can be configured at block 222to determine an appropriate treatment modality (for example,angioplasty, stenting, atherectomy, or the like), and/or determine asuggested treatment device for implantation (for example, 5 mm stent, 6mm stent, or other device). The recommendation and/or determination fromblock 222 can be outputted to the user at block 224.

As illustrated in FIG. 2, the physician can review the recommendationand/or determination from block 224 and perform the suggested procedureat block 228. Alternatively, a physician at point 226 could havedetermined the appropriate procedure and/or medical device to usewithout the assistance of the image enhancement and analysis system 110.At block 230, the image enhancement module 112 can be configured toretrieve, locate, obtain, receive an image, and/or locate the region ofinterest in the image automatically, semi-automatically, or the regionof interest can be located manually by the user. In certain embodiments,the region of interest can be focused on a particular portion of avessel such that the rest of the image can be cropped out of the image.At block 232, the image enhancement module 112 can be configured toperform image enhancement of the cropped image. In certain embodiments,the intra-procedure analysis engine module 116 can be configured atblock 234 to determine the length/distance of the object at issue, forexample, the degree to which an implanted stent is elongated beyond itsdesign length In certain embodiments, the intra-procedure analysisengine module 116 can be configured at block 236 to determine thecondition of the object at issue, for example the severity of radialcompression on a partially and/or completely implanted stent, in certainembodiments, the intra-procedure analysis engine module 116 can beconfigured at block 238 to determine other factors, attributes, and/orcharacteristics of the object at issue, for example, the diameter of apartially and/or completely implanted stent.

In reference to FIG. 2, the various factors, attributes,characteristics, and measurements obtained at blocks 234, 236, 238 arecompared with stored threshold values at block 240. In certainembodiments, the threshold values are stored in threshold database 242or other database 122. Based on the foregoing comparison, theintra-procedure analysis engine module 116 can be configured at block244 to determine and/or provide feedback and/or recommendation to thephysician performing the surgery (for example, accelerate sheathwithdrawal to more quickly deploy the stent, remove the stent (or ifremoval of the stent is not possible a warning or caution can beoutputted) due to irregular elongation, or the like). Other feedbackparameters can include without limitation positioning recommendations,technique recommendations, application of force recommendations,additional treatment recommendations and/or suggested use of othertools. The physician can continue the surgery based on the feedback andoptionally take another image of the patient and input into the imageenhancement module 112 at block 230, and repeat the foregoing process.Alternatively, the procedure or implantation is complete at block 246.Based on one or more of the last or final image and intra-proceduralimages of the patient, surgical site, and/or device, the intra-procedureanalysis engine module 116 can at block 248 output to the surgeon,physician, and/or user feedback, a success report, recommendations forimproving surgical technique.

As illustrated in FIG. 2, a physician and/or user, in certainembodiments, will want to monitor and/or review the condition or statusof an implanted object or device (for example, the condition of animplanted stent) at block 252. This process could proceed shortly afteror long after performing a procedure at block 246, or this process couldproceed shortly after or long after a procedure at point 250. At block254, the image enhancement module 112 can be configured to locate theregion of interest in the image automatically, semi-automatically, orthe region of interest can be located manually by the user. In certainembodiments, the region of interest can be focused on a particularportion of a vessel having an implanted stent, such that the rest of theimage can be cropped out of the image. At block 256, the imageenhancement module 112 can be configured to perform image enhancement ofthe cropped image. In certain embodiments, the post-procedure analysisengine module 118 can be configured at block 258 to determine thelength/distance of the object at issue, for example, the elongation ofan implanted stent. In certain embodiments, the post-procedure analysisengine module 118 can be configured at block 260 to determine thecondition of the object at issue, for example the severity of radialcompression by an artery or vessel on the stent. In certain embodiments,the post-procedure analysis engine module 118 can be configured at block262 to determine the activity of the object at issue, for example thevolume flow of an artery or vessel. In certain embodiments, thepost-procedure analysis engine module 118 can be configured at block 263to determine other factors, attributes, and/or characteristics of theobject at issue, for example, the degree to which a newly implantedstent overlaps a previously implanted stent.

In reference to FIG. 2, the various factors, attributes,characteristics, and measurements obtained at blocks 258, 260, 262, 263are compared with stored threshold values at block 264. In certainembodiments, the threshold values are stored in threshold database 266or other database 124. Based on the foregoing comparison, thepost-procedure analysis engine module 118 can be configured at block 268to determine the condition of the stent and/or the recommended treatmentmodality (for example, angioplasty, stenting, atherectomy, or the like),if any. The recommendation and/or determination from block 268 can beoutputted to the user at block 270.

With reference to FIG. 3, a general operating environment of anembodiment of an enhancement and an analysis system is illustrated. Asindicated, the process can begin at block 302 where at block 304 animage (for example, x-ray image) is created, by way of example, as aflat plate, cine, fluoroscopy, angiography, or the like. The image canbe an actual representation of a physical object, not an abstraction ofit like a regular photograph because, for example, the pixel intensityof an x-ray is a result of the sum of the attenuation coefficients inthe direction of an x-ray beam. If captured as an analog image, thisimage can be converted to a digital image for further processing. In themedical imaging context, this digital conversion can generally completedaccording to the Digital Imaging and Communications in Medicine (DICOM)standard.

With reference to FIG. 3, at block 306, the digital image in thisembodiment is processed by an image enhancement module 112 by, in thisembodiment, executing an image enhancement routine. The imageenhancement routine, as described in detail below, subtracts out thenoise (for example, background) from the image while maintaining dataintegrity, creating greater contrast between an region/area of interestand the background which allows for automated analysis of the image andallows for more effective manual analysis of the image. In thisembodiment, at block 308, the enhanced image can be analyzed by anautomated analysis/output engine which provides a physician moreaccurate and detailed information regarding a patient's state than aphysician could determine on his own by manually reviewing the patient'smedical image. For example, analysis engines 310, 312, 314 are depictedin block 308.

In reference to FIG. 3, such analysis engines could comprise adiagnostic/predictive engine (pre-procedure analysis engine module) 310which would be directed to pre-procedure image analysis for assessing apatient's health condition. Such diagnostic/predictive analysis could,among other things, comprise assessing the condition of a diseasedvessel (for example, assessing calcification, stenoses, vesselfragility, tortuosity, vessel compliance, geometry, or the like) anddetermining a course of therapy and/or recommendations based on thecondition of the diseased vessel (for example, PTA, atherectomy,stenting, devices, etc.). Such analysis engines could comprise afeedback/predictive engine (intra-procedure analysis engine module) 312which would be directed to procedure image analysis during a procedureto provide the physician with immediate feedback during the course of aprocedure (for example, determining the position of a stent duringplacement, reviewing the technique and forces in play, assessing whetheradditional treatment will be necessary, possibly using interactive aidesduring the course of the procedure to assist with the procedure). Suchanalysis engines could also comprise a quantitative/predictive engine(post-procedure analysis engine module) 314 which would be directed topost-procedure image analysis for assessing the efficacy of theprocedure. Such post-diagnostic analysis could, among other things,comprise assessing the durability of a placed stent; reviewing theplacement of the stent, or the like.

Referring now to FIG. 4, an embodiment of the process that can occurwhen the enhancement routine 306 is activated is illustrated. Theprocess can begin at block 402 wherein the process eitherreceives/obtains an image in a digital format at block 404, which canactivate the routine, or the process retrieves a stored image in adigital format at a user's request. Alternatively, the process canobtain a stored image in digital format and convert the image into astandardized format, such as DICOM, GIF, JPG, or the like. At block 406,the system can automatically and/or semi-automatically, or the user canmanually, select the region of interest and crop the image. In anembodiment, the region of interest is automatically determined havingthe system scan and/or analyze the image pixel line by pixel line toidentify areas of bone or other large areas of high intensity pixels. Bytagging the large areas of high intensity pixels, the system can cropout such areas from the image. One skilled in the art will appreciatethat there are other methodologies for automatically identifying theregion of interest and cropping the image. In an embodiments, the imageis cropped because if the image was not cropped to a region of interest,then portions of the body not pertinent to the analysis may skew theimage enhancement process. For example, if a variable intensity object,like bone, was left in the picture, it may skew the image measurementand, therefore, possibly disrupt enhancement. In certain embodiments,the image may be cropped so that only the contrasting objects relevantto the evaluation remain.

With regard to FIG. 4, the system can automatically and/orsemi-automatically identify the image background at block 408. Thebackground pixels can be subtracted by applying various methodologies,including without limitation the rolling ball radius method, the meanbackground value subtraction method, the seed segmentation method, orthe like. In an embodiment, the system employs the rolling ball radiusmethod wherein a local background value is determined for every pixel byaveraging over a very large ball around the pixel. This value ishereafter subtracted from the original image, removing large spatialvariations of the background intensities. The radius can be set to atleast the size of the largest object that is not part of the background.The radius parameter can be 1 pixel, and in an embodiment, the radiusparameter can be between 1-2 pixels.

In reference to FIG. 4, the system can automatically,semi-automatically, and/or manually normalize the pixels at block 410after cropping and subtracting the background pixels from the image atblock 408. One skilled in the art will appreciate the various ways tonormalize the pixel values of the resulting image. At block 412, thesystem can automatically, semi-automatically, and/or manually enhancethe image through a contract enhancement procedure. In an embodiment,the system can apply the Enhance Contrast procedure available in theImageJ program (the Image Processing and Analysis in Java program). Inan embodiment, for medical images, the parameters for the EnhanceContrast procedure are less than 2% saturation, and no histogramequalization. In an embodiment, the parameters are 1-2% saturation andno histogram equalization, and in further embodiments 1-1.1% saturationand no histogram equalization. Generally, if the image is noisy,improved results can be achieved with the saturation parameter less than2%.

With reference to FIG. 4, the system can optionally apply an unsharpmask filter at block 414. In an embodiment, the mask weight parameter ofthe unsharp mask filter is between 0.6-08. For high contrast images, themask weight parameter can be between 0.8-0.9, and for low contrastimages, the mask weight parameter can be at 0.6. This filter can be usedto sharpen the image quality and enable clear visualization of the finestructures in an object. Unsharp mask takes a pixel value of an imageand blurs that to an intended degree using a Gaussian blur function toproduce an unsharp mask. Then this mask is subtracted from the originalimage, and the contrast is normalized to compensate for the availablegray values.

As illustrated in FIG. 5, there is depicted an example series ofresulting images produced by one embodiment of the image enhancement andanalysis system 110. The example original image 502 illustrates a stentpositioned in front of a bone and next to a scale. In an embodiment, theexample original image 502 can be inputted into the image enhancementmodule 112, which can be configured to identify the region of interestby locating and cropping out the area of the image where the bone existsto produce the cropped image 504. The image enhancement module 112 canalso be configured to identify and subtract the background pixels in thecropped image to generate the background subtraction image 506. In anembodiment, the image enhancement module 112 is configured to apply acontrast enhancement to the background subtraction image to produce thecontrast enhancement image 508. Optionally, the image enhancement module112 can be configured to apply an unsharp mask filter to the contrastenhancement image 508 to generate the unsharp mask filter image 510(shown magnified in FIG. 5), which more clearly shows the stent, whichwas nearly undetectable in the original image. In the enhanced image,the user can clearly measure the diameter of the stent and/or seestenosis and/or radial compression of the stent and/or elongation of thestent, and/or any other feature.

In reference to FIGS. 6A and 6B, there is depicted another exampleoriginal medical image 602, and an example enhanced image 604 resultingfrom one embodiment of the image enhancement and analysis system 110.The original image 602 shows pelvic bone at the top of the image andfemoral bone on the left side. The region of interest is the stentlocated in the middle of the image. As can be seen, the stent is hardlyvisible in the original image 602. Further, in the highlighted area 606,a portion of the stent appears missing from the image. The pixel datafor the missing portion of the stent exists within the image; however,it is obfuscated within the original image 602 due to possibly veilingglare. By processing the image through the image enhancement andanalysis system 110, the missing portion of the stent and the rest ofthe stent can be made more visible as seen in the enhanced image 604.

With reference to FIG. 7A, there is illustrated an original image 702, acorresponding histogram 704 of the original image 702, and the imageexpressed in digital format by pixel value 706. In the context of 16 bitgray scale medical imaging, a histogram is a graphical representationshowing the frequency distribution of pixels across the possible 256shades of gray. The x-axis of a histogram represents the possible 256shades of gray that can be displayed in the image, and the y-axisrepresents the number of pixels existing in a particular shade of grayarea. One of ordinary skill in the art will understand that theinvention is not limited to use only with a “256” grayscale scale. A“256” scale is used herein by way of example only. The scale can be setbased on any number of grayscales present in the initial input image. Asdiscussed above with reference to FIG. 4, the system can be configuredto identify a region of interest (“ROI”) of the image and crop the imagearound that region at block 406. By way of example, an automated systemcould be set up to identify the ROI by discerning the differences inintensity between bones, stents and background. This step allows forfollow-on operations. In reference to FIG. 7B, there is illustrated anexample cropped image 708, a corresponding histogram 710 of the croppedimage 708 and the cropped image expressed in digital format by pixel712.

In reference to FIG. 8A, there is illustrated a histogram of the croppedimage 708. As discussed above, there are various methodologies forsubtracting out the background pixels, such as the rolling ball radiusmethod. In an embodiment, background subtraction can be achieved bydetermining the mode of the histogram, which is the gray scale valuethat has the most pixels. In the histogram of FIG. 8A, the mode is 204.Based on this methodology, the background pixels are subtracted byidentifying all pixels that have gray scale values of 204 and less, andreducing the gray scale value for each of those pixels to zero, therebyforcing those pixels to appear white in this example. This can equalizethe background pixel values and can make the only remaining portion ofthe histogram the values for the object of interest. Therefore, thebackground “noise” can be substantially neutralized and the object ofinterest can be focused on. Also, by using the histogram in thisfashion, the image of the object of interest does not get distorted oris not substantially distorted. It is still a true depiction of theimage; and therefore, the underlying data that is embedded in the imageis not corrupted, and the image can still be used as an assessment tool.

With reference to FIG. 8B, the histogram in this process can benormalized back to a “256” scale as indicated with respect to block 410in FIG. 4. The mode value of the background “204” is set to the “256”value which shifts the histogram in this example by “52”, but keeps therelationship of the values intact. The background value may besubtracted from the entire image. (Note: There are no pixel values thatare significantly less than the background value so there are very fewnegative values that are approximated as 0). After subtraction, thepixel values are reduced to Actual Pixel Intensity—Background Intensity.In order to display the image back on a 256 level gray scale, the pixelintensity values are normalized with respect to the maximum pixelintensity value. An example normalized image, with the backgroundsubtracted, is depicted in FIG. 9A, 902, along with a correspondinghistogram 904 and a digital representation by pixel value 906. Anadvantage of this process is that now the noise of the background isfiltered out while retaining the true physical relationship of theunderlying image for the ROI. Meaningful automated analysis andenhancement can now be performed on the normalized image because thebackground noise does not skew this analysis and image enhancement. Atthis point, the normalized image can be used for any purpose that an enduser wants to use it for. In the example depicted, the process at block412 in FIG. 4 uses the normalized image to enhance the image. Withreference to FIG. 9B, there is illustrated an example contrast enhancedimage 908, a corresponding histogram 910, and a digital representationby pixel value 912. In an embodiment, the normalized digital image 902has been enhanced by applying an intensification value across the entireimage to highlight the contrast between elements. By doing theenhancement this way, the original underlying data is not distorted, andthe values of the histogram remain constant relative to one another.

FIG. 10 depicts example images of stent elongation. The original image1002 was processed by an embodiment of the image enhancement andanalysis system 110 to produce the enhanced image 1004 using thetechniques discussed above. In this example, the image enhancement andanalysis system 110, using the techniques described below, determinedthat the entire stent was elongated substantially across its entirelength. In an embodiment, the elongation of the stent is determined byusing the guidewire 1006 as a known scale for measuring the length thestent because the guidewire diameter and partial length are known andcan be used as a reference point in measuring the stent. Othermethodologies and/or techniques are discussed below for determiningstent elongation.

With reference to FIG. 11, there are illustrated example images ofstenosis. In the original image 1102, the area of stenosis 1106 cannoteasily be seen; however, in the enhanced image 1104, the stenosis area1106 is more clearly visible such that a system can more accuratelymeasure the diameter of the stent. In an embodiment, the system isconfigured to identify stent struts by detecting high intensity pixelvalues in the image, and then measuring the distance between struts todetermine the diameter of the stent. Generally oversized stents areimplanted into vessels so that stents have enough outward radial forcefor supporting a lumen structure. To determine degree of or diameter ofstenosis, the measured diameter value can be compared to a normal stentor a normal implanted stent. Using the ratio of the measured diametervalue to the normal stent diameter value, the image enhancement andanalysis system 110 can correlate the ratio to a database and/or lookupchart listing the known outward radial force exerted by a stent of thiskind so as to infer the radial force being exerted by the vessel on thestent. Degree of or diameter of stenosis can also be determined bycomparing the measured diameter to the relative diameter of theimplanted stent, or the minimal lumen diameter (MLD). With thismethodology, the image enhancement and analysis system measures thediameter of the stent at substantially each line profile to determinethe maximum MLD, which is generally the largest measured diameter of thestent. To determine the level of stenosis of the stent/vessel, themeasured diameter of the stent at a particular area is divided by themax MLD to determine the percent residual stenosis.

In reference to FIG. 12, there are illustrated example images ofcalcification. To determine vessel calcification, the image enhancementand analysis system 110 can be configured to perform a four criteriaanalysis. First the system determines whether there is a depression inthe stent, for example, a localized region in the stent having reduceddiameter, and whether the areas surrounding the depression have pixelswith varied intensity (usually darker than surrounding pixels). Todetermine whether a depression exists, the system performs slopeanalysis of the outer boundary of the stent. For example, the system canbe configured to detect changes in slope values, and can be configuredto mark, demark, and/or identify the areas/points of changing slope (forexample, points 1206, 1208, and 1210). Here, the system can interpretthe change in slope values as indicating that a depression existsbetween these two points 1206, 1210. The system can be configured toperform line profile analysis to determine whether the area surroundingthe depression has pixels with varied intensity (usually darker thanother pixels) by evaluating the pixel intensity values.

With reference to FIG. 12, as a second criterion, the system can beconfigured to determine whether the included angle between the twopoints 1206, 1210 is less than 180 degrees, and more preferably lessthan 150 degrees. As a third criterion, the system can be configured todetermine whether the height-width ratio is less than certain clinicallyrelevant values. As a fourth criterion, the system can be configured todetermine whether the surrounding pixel intensity levels are betweenmeasured clinically relevant pixel values. In an embodiment, the systemis configured to identify vessel calcification if all four criteria aresatisfied. To determine whether the measured pixel intensity value isabove the above threshold values, the threshold values should becalibrated to the measured pixel intensity values. To calibrate thethreshold values, the system can be configured to determine the measuredpixel intensity value of the strut in the image and correlate that valueto the strut pixel intensity value determined for the threshold values.Based on that correlation and cross multiplication, the threshold valuescan be calibrated for the image at issue. With the threshold valuescalibrated, the system can determine whether the measured pixelintensity values are above the calibrated threshold values. In anembodiment, the system can be configured to identify an area asexhibiting characteristics of calcification if all four of the criteriaare satisfied. In other embodiments, each of the four criteria can beoptional to a finding of calcification by the system.

With reference to FIGS. 13A, 13B, and 13C, there are illustrated exampleimages depicting areas of stent radial compression and areas of stentelongation. In FIG. 13A, the image enhancement and analysis system 110identified in the enhanced image two regions of radial compression 1302,1304. In an embodiment, the image enhancement and analysis system 110 isconfigured to determine the diameter of the stent (for example, at block236) and compare the measured diameter to a threshold values database(for example, database 242) to correlate the measured diameter to ascore, category, and/or grade based on the type, size, and/ormanufacture of the stent at issue. For example, an embodiment of ascoring, categorizing, typing, and/or grading system for residualstenosis is illustrated in Table 1 below, and such data can be stored inthe image enhancement and analysis system 110, and can be unique withrespect each kind, type, manufacture of stent.

TABLE 1 Radial Compression Grading System Grade Percentage of RadialCompression Grade 0 No or substantially no residual stenosis detectedGrade I 1%-40% Grade II 41%-100%

As illustrated in FIG. 13A, the image enhancement and analysis system110 categorized both regions of compression 1302, 1304 as type II. In anembodiment, the image enhancement and analysis system 110 is configuredto output a recommendation to the user based on the categorization ofthe stent diameter. For example, if the stent is categorized as being ina type 0 or type I condition, then the image enhancement and analysissystem 110 can be configured to output a recommendation to the physicianto continue monitoring the stent every six months for possible furtherradial compression because based on clinical outcome studies and otherresearch, it has been, in an example, determined, statisticallydetermined, and/or clinically determined that there is a correlationbetween a type 0 or type I condition, and high patency/high flow.Another example is, if the stent is categorized as being in a type IIcondition, then the image enhancement and analysis system 110 can beconfigured to output a recommendation to the physician to revise,reposition, reorient, realign, redeploy, move, push, compress, alter,expand, re-insert, remove, and/or replace, and/or prepare the vesselfurther before placing the stent because based on clinical outcomestudies and other research, it has been, in an example, determined,statistically determined, and/or clinically determined that there is acorrelation between type II radial compression and vessel patency.

With reference to FIG. 13B, the image enhancement and analysis system110 identified in the enhanced image one area of radial compression1306, and two regions of stent elongation 1308, 1310. With respect tothe stent elongation, the image enhancement and analysis system 110 canbe configured to determine the length of the stent (for example, atblock 234) and compare the measured length to a threshold valuesdatabase (for example, database 242) to correlate the measured length toa score, category, and/or grade based on the type, size, and/ormanufacture of the stent at issue. For example, an embodiment of ascoring, categorizing, typing, and/or grading system for stentelongation is illustrated in Table 2 below, and such data can be storedin the image enhancement and analysis system 110, and can be unique withrespect each kind, type, manufacture of stent.

TABLE 2 Elongation Grading System Grade Percentage of Elongation Grade I 0%-10% Grade II 11%-20% Grade III 21% or more

As illustrated in FIG. 13B, the image enhancement and analysis system110 categorized elongation region 1308 as type II elongation, andelongation region 1310 as type I elongation. In an embodiment, the imageenhancement and analysis system 110 is configured to output arecommendation to the physician and/or user based on the categorizationof the stent elongation. For example, if the stent is categorized asbeing in a type II or type III condition, then the image enhancement andanalysis system 110 can be configured to output a recommendation to thephysician to revise, reposition, reorient, realign, redeploy, move,push, compress, alter, expand, re-insert, remove, and/or replace, and/ordeploy the stent correctly because based on clinical outcome studiesand/or other research, it has been, in an example, determined,statistically determined, and/or clinically determined that there is acorrelation between type II and type III elongation, and stent fracture.Another example is, if the stent is categorized as being in a type 0 ortype I condition, then the image enhancement and analysis system 110 canbe configured to output a recommendation to the physician to continuemonitoring the stent every six months for possible further elongationbecause based on clinical outcome studies and other research, it hasbeen, in an example, determined, statistically determined, and/orclinically determined that there is a reduced risk of stent fracturewhen the stent is in a type 0 or type I condition.

Figures similar to FIGS. 13A, 13B, and 13C can be used to demonstratestent overlap. Stent overlap can occur in instances where a physiciandeploys multiple stents in an area, and the stents overlap each other.In an embodiment, the image enhancement and analysis system 110 can beconfigured to determine the length of the stent overlap (for example, atblock 238) and compare the measured overlap length to a threshold valuesdatabase (for example, database 242) to correlate the measured overlaplength to a score, category, and/or grade based on the type, size,and/or manufacture of the stent at issue. For example, an embodiment ofa scoring, categorizing, typing, and/or grading system for stent overlapis illustrated in Table 3 below, and such data can be stored in theimage enhancement and analysis system 110, and can be unique withrespect each kind, type, manufacture of stent.

TABLE 3 Stent Overlap Grading System Grade Percentage of Stent OverlapGrade 0 0%-3% Grade I 4% or more

Table 3, as well as the other similar tables disclosed herein is anexample of grading systems or definitions related to variouscharacteristics of a device, such as a stent, and that other valuesand/or threshold values can be used, and/or can be specific to devices.For example, the percentage of stent overlap ranges can be as follows:Grade 0=0%-4%; Grade I=4.1% or more. In other embodiments, the thresholdvalue ranges for stent overlap from Grade 0 can range from 0 mm-5 mm,and for Grade I the threshold value ranges for stent overlap from 1mm-20 mm. In an embodiment, for a particular stent, Grade 0=0 mm-3 mm,and Grade I=greater than 3 mm.

Similar to FIGS. 13A, 13B, and 13C, the image enhancement and analysissystem 110 can identify stent overlap by identifying the stent tipmarkers of one stent and measure the distance between the stent tipmarkers of a second stent that is overlapping the first stent. The imageenhancement system can be configured to determine the total length ofthe first and/or second stent. To determine the percentage of stentoverlap, the image enhancement system divides the measured overlaplength by either the total length of the first/second stent or theaverage of the first and second stent total length.

As discussed with FIGS. 13A and 13B, the image enhancement and analysissystem 110 can be configured to categorize the measured overlap lengthas either type 0 or type I. In an embodiment, the image enhancement andanalysis system 110 is configured to output a recommendation to thephysician and/or user based on the categorization of the stent overlap.For example, if the stent is categorized as being in a type I condition,then the image enhancement and analysis system 110 can be configured tooutput a recommendation to the physician to revise, reposition,reorient, realign, redeploy, move, push, compress, alter, expand,re-insert, remove, and/or replace, and/or prevent too much overlapping,the stents because based on clinical outcome studies and other research,it has been, in an example, determined, statistically determined, and/orclinically determined that there is a correlation between type I stentoverlap, and stent fracture. Another example is, if the stent iscategorized as being in a type 0 condition, then the image enhancementand analysis system 110 can be configured to output a recommendation tothe physician to continue monitoring the stent every six months forpossible stent fracture because based on clinical outcome studies andother research, there appears to be a correlation between a reduced riskof stent fracture and a type 0 condition.

In reference to FIG. 14A, an example normal stent 1400 is illustrated,and FIG. 14B depicts a magnified view 1402 of the example stent. Stentsare widely used for supporting a lumen structure in a patient's body.For example, stents may be used to maintain patency (flow) of a coronaryartery, carotid artery, cerebral artery, popliteal artery, iliac artery,femoral artery, tibial artery, other blood vessels including veins, orother body lumens such as the ureter, urethra, bronchus, esophagus, orother passage. Stents are commonly metallic tubular structures made fromstainless steel, Nitinol, Elgiloy, cobalt chrome alloys, tantalum, andother metals, although polymer stents are known. Stents can be permanentenduring implants, or can be bioabsorbable at least in part.Bioabsorbable stents can be polymeric, bio-polymeric, ceramic,bio-ceramic, or metallic, and may elute over time substances such asdrugs. Non-bioabsorbable stents may also release drugs over time. Stentsare passed through a body lumen in a collapsed state. At the point of anobstruction or other deployment site in the body lumen, the stem isexpanded to an expanded diameter to support the lumen at the deploymentsite.

With reference to FIG. 14A, certain stents designs are open-celled orclose-celled cylindrical structures that are expanded by inflatableballoons at the deployment site. This type of stent is often referred toas a “balloon expandable” stent. Stent delivery systems for balloonexpandable stents are typically comprised of an inflatable balloonmounted on a multi lumen tube. The stent delivery system with stentcrimped thereon can be advanced to a treatment site over a guidewire,and the balloon inflated to expand and deploy the stent. Other stentsare so-called “self expanding” stents and do not use balloons to causethe expansion of the stent. An example of a self-expanding stent is atube (for example, a coil tube, a mesh tube, or an open-celled tube)made of an elastically deformable material (for example, a superelasticmaterial such a Nitinol). This type of stent is secured to a stentdelivery device under tension in a collapsed state. At the deploymentsite, the stent is released so that internal tension within the stentcauses the stent to self-expand to its enlarged diameter. Otherself-expanding stents are made of so-called shape-memory metals. Suchshape-memory stents experience a phase change at the elevatedtemperature of the human body. The phase change results in expansionfrom a collapsed state to an enlarged state. A very popular type of selfexpanding stent is an open-celled tube made from superelastic Nitinol,for example, the Protege GPS stent from ev3, Inc. of Plymouth, Minn.Another stent design is disclosed in U.S. Patent Publication No.20070289677, titled IMPLANT HAVING HIGH FATIGUE RESISTANCE, DELIVERYSYSTEM, AND METHOD OF USE, filed Jun. 18, 2007, which herebyincorporated by reference in its entirety, including specifically butnot limited to the embodiments related to implants having high fatigueresistance. With reference to FIG. 14C is the example magnified view1402 of the example stent 1400 highlighting example cells 1404, 1406,1408 of the stent. In general stents can be constructed of cells havedouble helical spiral pattern.

As discussed above, there are other methods and techniques fordetermining stent elongation as will now be discussed in reference toFIG. 15. In an embodiment, stent elongation can be determined bytemplate matching, wherein the image enhancement and analysis system 110is configured to perform a pixel line by pixel line analysis of theimage and compare it to a corresponding pixel line in an image of anormal stent. If the pixel lines match, then the image enhancement andanalysis system 110 can determine that the stent at issue does notexhibit stent elongation; otherwise the stent does exhibit stentelongation. Alternatively, the image enhancement and analysis system 110need not be configured to match every pixel line in order to speed upthe processing and analysis.

With reference to FIG. 15, there is illustrated a magnified view of theexample stent, and the example distances and angles that the imageenhancement and analysis system 110 can be configured to measure todetermine stent elongation. For example, the image enhancement andanalysis system 110 at block 234 can be configured to measure length Abetween a vertex of a peak in the top portion of a cell and a vertex ofa valley in the bottom portion of the same cell. In an embodiment, theimage enhancement and analysis system 110 at block 234 is configured tomeasure the length B between a vertex of a valley in the top portion ofa cell and a vertex of a peak in the bottom portion of the same cellwhere the changes in length B can generally be more pronounced than thechanges in length A. The image enhancement and analysis system 110 atblock 240 can be configured to compare the measured lengths A and/or Bto threshold values stored in database 242, and based on the comparisonthe image enhancement and analysis system 110 can be configured tocategorize the elongation and/or determine a recommendation to output tothe physician and/or user.

With reference to FIG. 15, the image enhancement and analysis system 110at block 234 can be configured to measure angles C, D, and F in thevertexes of peaks and valleys in a cell, and the angles near the cellinterconnection areas. The image enhancement and analysis system 110 canbe configured to measure a single angle D, or determine the average fora plurality of angle D's. In an embodiment, example Table 4 belowcorrelates the measured angle D values to percent elongation of thestent, and the image enhancement and analysis system 110 can beconfigured to store such data, and such data can be unique with respecteach kind, type, manufacture of stent.

TABLE 4 Measure Angle D Correlations to Percent Elongation AngleMeasurement of Angle D Percent Elongation of Stent 30°-40° Normal26°-29° ~10%-15% 22°-25° ~15%-30% 18°-21° ~30%-50%

In reference to FIG. 15, in an embodiment, the image enhancement andanalysis system 110 at block 234 can be configured to measure the angleC and/or F where the changes in degrees are generally more pronounced inangles C and/or F than in angles D. In an embodiment, example Table 5below correlates the measured angle F values to percent elongation ofthe stent, and the image enhancement and analysis system 110 can beconfigured to store such data.

TABLE 5 Measure Angle D Correlations to Percent Elongation AngleMeasurement of Angle F Percent Elongation of Stent 30°-49° ~10% 50°-59°~20% 60°-69° ~30% 70°-79° ~40% 80°-90° ~50%

With reference to FIG. 15, the image enhancement and analysis system 110at block 240 can be configured to compare the measured angles tothreshold values stored in database 242, and based on the comparison theimage enhancement and analysis system 110 can be configured tocategorize the elongation and/or determine a recommendation to output tothe physician and/or user. One of ordinary skill the art will appreciatethe methods and techniques for measuring angles C, D, and F. Forexample, the image enhancement and analysis system 110 can be configuredto use a Hough transform analysis to find the best fit line (vector) astent strut to be measured, and generating/identifying a known vector E,and by taking the dot product of both vectors, the image enhancementanalysis system 110 can generate (automatically or semi-automatically)measurement of the angle F.

With reference to FIG. 16A, there is depicted an example normal stent,and in FIG. 16C there is depicted a corresponding example single sliceprofile of the normal stent. FIG. 16B depicts an example implantedstent, and FIG. 16D depicts a corresponding example single slice profileof the implanted stent. In this example, an image is first obtained asindicated at block 404 (FIG. 4). The image is then enhanced as indicatedat blocks 406, 408, 410, 412, and 414 (optional). In this example,referring now to FIGS. 16A and 16B, the process then does a raster scanof the entire image as the image is generally expressed in digitalintensity values. FIGS. 16C and 16D show the raster scan respectivelyfor FIGS. 16A and 16B. Once the profile of a slice is obtained, theprocess then identifies the regions where the stent lies to determinemetal coverage of the line profile. Generally there are number of peakslower/higher in magnitude in a certain area. In an embodiment, the peaksindicate the regions where a stent strut exists.

With reference FIGS. 16A, 16B, 16C, and 16D, the process then selectsthree distinct peaks, generally the edges and the center. Using thesepeaks as a source of reference, the process calculates the Euclideandistance between these high intensity peaks as the x and y co-ordinateof these peaks are known. Finally, the process normalizes this distanceto match the template of the original stent. The differences in sliceprofiles of normal stents as compared to the elongated stent are shownin FIGS. 16C and 16D respectively to elucidate the differences and helpcategorize the elongation of the device better. In an embodiment, theenhancement and analysis system 110 can be configured to determine themetal coverage of the line profile, and if the metal coverage of theline profile is below a threshold value, then the enhancement andanalysis system 110 can be configured to output a recommendation to thephysician and/or user to remove the stent. Alternatively, each singleslice profile can act like a signature for the stent, and the imageenhancement and analysis system 110 can be configured to compare andbest match a signature of a stent at issue with the signatures of knownstents (stored in threshold database 242, for example). In determining amatch or best fit match, the image enhancement and analysis system 110can extrapolate stent elongation for the stent at issue based on knowncharacteristics of the known stent.

FIG. 17A depicts an example image of a stent having example regions ofinterest, and FIG. 17B depicts an example representation of plaquehardness estimations for the example regions of interest depicted inFIG. 17A. In this example, an image is first obtained as indicated atblock 404. The image is then enhanced as indicated at blocks 406, 408,410, 412, and 414 (optional). In this example, referring now to FIG.17A, the process then measures the pixel intensity of the guidewire orany other calibration object, and the pixel intensity of the areasaround the object of interest at different regions R1, R2, R3 along thelength of the object, and plots the histogram to measure the mean pixelintensity value and the standard deviation.

The system can then calculate the relative intensity of the calcifiedregions with respect to the intensity of the calibration object, theguidewire in this case, as shown in FIG. 17B. FIG. 17B indicates that R1may have relatively less or softer calcification as compared to regionsR2 and R3 when calibrated with the pixel intensity of the guidewire.

With reference to FIG. 18A, there is illustrated an example imagedepicting contrast flow before stenting, and in reference to FIG. 18B,there is illustrated an example image depicting contrast flow afterstenting or during atherectomy. In this example, cities of contrastinjections in the vessel before and after treatment are obtained atblock 404 (FIG. 4). The process then enhances the cine of theangiographic contrast injection as indicated at blocks 406, 408, 410,412, and 414 (optional). In this example, referring now to FIGS. 18A and18B, the process selects ROI's at the input and output of the vessel forboth before and after treatment cines. The process then calculates timedensity curves (TDC's) for each region as the injected contrast passesthrough the region, and then calculates discrete Fourier transforms foreach TDC. The process then calculates the Transfer function by dividingoutput waveform by input waveform in frequency space, and then takes aninverse Fourier transform for both cines before and after treatment. Theprocess then integrates the final transfer function for both cinesbefore and after treatment and performs a relative comparison to providea quantitative parameter indicating the difference in flow waveformafter treatment. The difference in contrast flow TDC between unstentedand stented vessels in this example, shown in FIGS. 18A and 18B, aftercalculation is 6%.

With reference to FIG. 19, there is illustrated an example chartdepicting flow dynamics of a vessel that is stented versus unstented. Inan embodiment, the image enhancement and analysis system 110 can beconfigured to determine flow rate and/or patency through a vessel bymeasuring at different time intervals the pixel intensity in a vesselinjected with a bolus of radiopaque and/or other dye, and the measuredpixel intensity values (y-axis) can be plotted against time (x-axis) asillustrated in FIG. 19. In an embodiment, the measured pixel values arenormalized because in some instances the injected quantities and/orrates of radiopaque and/or other dye into the vessel is not the same. Tonormalize the pixel values, the total mass flow rate is measured at aposition along the vessel (for example, the top portion of the vessel),and the image enhancement and analysis system 110 can be configured todivide each measured pixel value by the total mass flow rate tonormalize the pixel value. In an embodiment, the image enhancement andanalysis system 110 can be configured to integrate the curves todetermine the areas under the stented vessel curve and the unstentedvessel curve. The image enhancement and analysis system 110 can thendetermine the percent change between the two areas, and this percentchange can then be correlated to determined threshold values todetermine whether the stenting procedure was successful or not, forexample, successful in increasing blood flow through the vessel. Alongthe same lines one can also develop metrics from the waveform likewashout rate, input rate, and peak intensity to help analyze the factorson which a clinical outcome would depend upon.

FIG. 20A is an example chart depicting the deployment rate of a stent,and FIG. 20B is a corresponding series of example images of a stentdeployment. In this example, x-ray cine is first obtained. The cinesequence is then enhanced as indicated at blocks 404, 406, 408, 410,412, and 414 (optional). In an embodiment, a delivery catheter innertube tip marker A and an delivery system outside sheath tip marker B arecalibrated to a given intensity, and a motion tracking algorithm is usedto follow the motion of marker B, and determine the distance betweenmarker A and marker B for the images in the sequence. Generally, theinner tube tip and the tip of the sheath have band markers withcharacteristics, such as X-ray absorbance, to cause a significant pixelintensity difference on the image, making it relatively easy after imageenhancement for motion tracking of the markers from the surroundingimage background. Some of the images of the withdrawal of the outsidesheath are shown in FIG. 20B and the corresponding deployment rate curveis shown in FIG. 20A. In certain embodiments, the system can beconfigured to provide to the user/physician real-time or substantiallyreal-time feedback as to whether to speed up or slow down deployment ofthe stent. In certain embodiments, if the stent is deployed too slowlyor too fast, then stent elongation, fracture, and other adverse affectscan occur. Additionally, by deploying the stent at an optimal speed, thephysician can minimize radiation exposure of the patient undergoing theprocedure because the fewer x-ray images that need be obtained thefaster the procedures proceeds. In general, it may be preferable todeploy the stent slowly at the beginning of the deployment, then morequickly during the middle of the deployment, and then more slowly at theend of the deployment.

FIG. 21 is an example form 2100 used for generating and/or collectingclinical data. In an embodiment, the computer system can be configuredto automatically use and/or populate and/or input data into the form,and in an embodiment, the form can be completed by a user or physician,and/or the form/data can be upload/inputted into the system.Researchers, scientists, physicians, and other users can use the exampleform 2100 in reviewing images to correlate clinical outcomes associatedwith certain features, characteristics, measurements, distances, and thelike observed in enhanced images produced by the image enhancement andanalysis system 110. For example, in reviewing images having a stent,each centimeter of the stent can be reviewed, and an appropriate box canbe checked or an entry can be made for each centimeter position alongthe length of the stent. For example, in the form 2100 at centimetermarks 33, 34, 35, and 36 the user of the form noted that there existedradial compression of the stent. Further, at centimeter mark 36 therewas a fracture. Such data from the form 2100 can then be stored in adatabase, for example, master database 138, whereon data mining could beperformed to determine threshold values that are predictive of stentelongation, fractures, or any other adverse or undesirable condition ofa stent.

Computing System

In some embodiments, the systems, computer clients and/or serversdescribed above take the form of a computing system 2200 shown in FIG.22, which is a block diagram of one embodiment of a computing system(which can be a fixed system or mobile device) that is in communicationwith one or more computing systems 2217 and/or one or more data sources2219 via one or more networks 2210. The computing system 2200 may beused to implement one or more of the systems and methods describedherein. In addition, in one embodiment, the computing system 2200 may beconfigured to process image files. While FIG. 22 illustrates oneembodiment of a computing system 2200, it is recognized that thefunctionality provided for in the components and modules of computingsystem 2200 may be combined into fewer components and modules or furtherseparated into additional components and modules.

Client/Server Module

In one embodiment, the system 2200 comprises an image processing andanalysis module 2206 that carries out the functions, methods, and/orprocesses described herein. The image processing and analysis module2206 may be executed on the computing system 2200 by a centralprocessing unit 2204 discussed further below.

Computing System Components

In one embodiment, the processes, systems, and methods illustrated abovemay be embodied in part or in whole in software that is running on acomputing device. The functionality provided for in the components andmodules of the computing device may comprise one or more componentsand/or modules. For example, the computing device may comprise multiplecentral processing units (CPUs) and a mass storage device, such as maybe implemented in an array of servers.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, C or C++, or the like. A softwaremodule may be compiled and linked into an executable program, installedin a dynamic link library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, Lua, or Python.It will be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software instructions may be embedded infirmware, such as an EPROM. It will be further appreciated that hardwaremodules may be comprised of connected logic units, such as gates andflip-flops, and/or may be comprised of programmable units, such asprogrammable gate arrays or processors. The modules described herein canbe implemented as software modules, but may be represented in hardwareor firmware. Generally, the modules described herein refer to logicalmodules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

In one embodiment, the computing system 2200 also comprises a mainframecomputer suitable for controlling and/or communicating with largedatabases, performing high volume transaction processing, and generatingreports from large databases. The computing system 2200 also comprises acentral processing unit (“CPU”) 2204, which may comprise a conventionalmicroprocessor. The computing system 2200 further comprises a memory2205, such as random access memory (“RAM”) for temporary storage ofinformation and/or a read only memory (“ROM”) for permanent storage ofinformation, and a mass storage device 2201, such as a hard drive,diskette, or optical media storage device. Typically, the modules of thecomputing system 2200 are connected to the computer using a standardsbased bus system. In different embodiments, the standards based bussystem could be Peripheral Component Interconnect (PCI), Microchannel,SCSI, Industrial Standard Architecture (ISA) and Extended ISA (EISA)architectures, for example.

The exemplary computing system 2200 comprises one or more commonlyavailable input/output (I/O) devices and interfaces 2203, such as akeyboard, mouse, touchpad, and printer. In one embodiment, the I/Odevices and interfaces 2203 comprise one or more display devices, suchas a monitor, that allows the visual presentation of data to a user.More particularly, a display device provides for the presentation ofGUIs, application software data, and multimedia presentations, forexample. In the embodiment of FIG. 22, the I/O devices and interfaces2203 also provide a communications interface to various externaldevices. The computing system 2200 may also comprise one or moremultimedia devices 2202, such as speakers, video cards, graphicsaccelerators, and microphones, for example.

Computing System Device/Operating System

The computing system 2200 may run on a variety of computing devices,such as, for example, a server, a Windows server, an Structure QueryLanguage server, a Unix server, a personal computer, a mainframecomputer, a laptop computer, a cell phone, a personal digital assistant,a kiosk, an audio player, and so forth. The computing system 2200 isgenerally controlled and coordinated by operating system software, suchas z/OS, Windows 95, Windows 98, Windows NT, Windows 2000, Windows XP,Windows Vista, Linux, BSD, SunOS, Solaris, or other compatible operatingsystems. In Macintosh systems, the operating system may be any availableoperating system, such as MAC OS X. In other embodiments, the computingsystem 2200 may be controlled by a proprietary operating system.Conventional operating systems control and schedule computer processesfor execution, perform memory management, provide file system,networking, and I/O services, and provide a user interface, such as agraphical user interface (“GUI”), among other things.

Network

In the embodiment of FIG. 22, the computing system 2200 is coupled to anetwork 2210, such as a LAN, WAN, or the Internet, for example, via awired, wireless, or combination of wired and wireless, communicationlink 2215. The network 2210 communicates with various computing devicesand/or other electronic devices via wired or wireless communicationlinks. In the exemplary embodiment of FIG. 22, the network 2210 iscommunicating with one or more computing systems 2217 and/or one or moredata sources 2219. The network may include one or more of internetconnections, secure peer-to-peer connections, secure socket layer (SSL)connections over the internet, virtual private network (VPN) connectionsover the internet, or other secure connections over the internet,private network connections, dedicated network connections (for example,ISDN, T1, or the like), wireless or cellular connections, or the like orany combination of the foregoing.

Access to the image processing and analysis module 2206 of the computersystem 2200 by computing systems 2217 and/or by data sources 2219 may bethrough a web-enabled user access point such as the computing systems'2217 or data source's 2219 personal computer, cellular phone, laptop, orother device capable of connecting to the network 2210. Such a devicemay have a browser module is implemented as a module that uses text,graphics, audio, video, and other media to present data and to allowinteraction with data via the network 2210.

The browser module or other output module may be implemented as acombination of an all points addressable display such as a cathode-raytube (CRT), a liquid crystal display (LCD), a plasma display, or othertypes and/or combinations of displays. In addition, the browser moduleor other output module may be implemented to communicate with inputdevices 2203 and may also comprise software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements such as, for example, menus, windows, dialog boxes,toolbars, and controls (for example, radio buttons, check boxes, slidingscales, and so forth). Furthermore, the browser module or other outputmodule may communicate with a set of input and output devices to receivesignals from the user.

The input device(s) may comprise a keyboard, roller ball, pen andstylus, mouse, trackball, voice recognition system, or pre-designatedswitches or buttons. The output device(s) may comprise a speaker, adisplay screen, a printer, or a voice synthesizer. In addition a touchscreen may act as a hybrid input/output device. In another embodiment, auser may interact with the system more directly such as through a systemterminal connected to the score generator without communications overthe Internet, a WAN, or LAN, or similar network.

In some embodiments, the system 2200 may comprise a physical or logicalconnection established between a remote microprocessor and a mainframehost computer for the express purpose of uploading, downloading, orviewing interactive data and databases on-line in real time. The remotemicroprocessor may be operated by an entity operating the computersystem 2200, including the client server systems or the main serversystem, an/or may be operated by one or more of the data sources 2219and/or one or more of the computing systems. In some embodiments,terminal emulation software may be used on the microprocessor forparticipating in the micro-mainframe link.

In some embodiments, computing systems 2217 who are internal to anentity operating the computer system 2200 may access the imageprocessing and analysis module 2206 internally as an application orprocess run by the CPU 2204.

User Access Point

In one embodiment, a user access point comprises a personal computer, alaptop computer, a cellular phone, a GPS system, a Blackberry® device, aportable computing device, a server, a computer workstation, a localarea network of individual computers, an interactive kiosk, a personaldigital assistant, an interactive wireless communications device, ahandheld computer, an embedded computing device, or the like.

Other Systems

In addition to the systems that are illustrated in FIG. 22, the network2210 may communicate with other data sources or other computing devices.The computing system 2200 may also comprise one or more internal and/orexternal data sources. In some embodiments, one or more of the datarepositories and the data sources may be implemented using a relationaldatabase, such as DB2, Sybase, Oracle, CodeBase and Microsoft® SQLServer as well as other types of databases such as, for example, a flatfile database, an entity-relationship database, and object-orienteddatabase, and/or a record-based database.

All of the methods and processes described above may be embodied in, andfully automated via, software code modules executed by one or moregeneral purpose computers or processors. The code modules may be storedin any type of computer-readable medium or other computer storagedevice. Some or all of the methods may alternatively be embodied inspecialized computer hardware.

Other Applications

By creating a normalized image, with the background noise subtracted asdescribed above, this normalized image can used in a wide variety ofanalysis applications, in addition to just image enhancement. Examplesof such applications comprise any image guided intervention analysis. Insuch situations, the process of this invention not only enhances theimage, but also provides unique ways of quantifying data that have beenexpressed only qualitatively to date.

For example, the image enhancement and analysis system 110 can beconfigured to perform other stenting analyses in other situations, forexample, cardiovascular, peripheral, neurovascular, renal, SMA (SuperiorMesenteric Artery), gastric, SFA, popliteal, iliac, or the like.Additionally, the image enhancement and analysis system 110 can beconfigured to determine, analyze, and generate recommendations/reportsrelating to other diseased vessel conditions, such as but not limited tocalcification, stenoses, vessel fragility, tortuosity, compliance, andgeometry. Based on an analysis of diseased vessel conditions, the imageenhancement and analysis system 110 can be configured to determine anappropriate vessel therapy that can include without limitation PTA,atherectomy, thrombectomy, stenting, and the like. Further, the imageenhancement and analysis system 110 can be configured to determine,measure, evaluate, analyze other factors as listed in Table 6 in orderto generate a recommendation, report, and/or provide feedback.

TABLE 6 Other Factors Stent Additional Vessel Structure Structure StentDurability Information Stenosis Fracture Forces on stent PhysicianCalcification Elongation struts deployment (soft, hard, CompressionLocation technique levels) Torsion Overall stability Image TortusityOverlap Template resolution Dimensions Dimensions matching PressureLocations Location Vessel compliance waveforms Compliance Offsetsmismatch In-vivo stent Patency Migration Pinning points (strut behavior(before) Wall apposition location with respect Restenosis Patency(after) to plaque)

In addition to stenting analysis, the image enhancement and analysissystem 110 can be configured to for use in other areas that can includewithout limitation:

-   -   1. CRM (Cardiac rhythm management); lead analysis    -   2. Orthopedics        -   a. Assess body geometry        -   b. Prostheses measurement    -   3. Emergency Medicine: Assess Fractures    -   4. Cosmetic Surgery    -   5. Forensic Investigation    -   6. Oncology Tumor Extent Investigation    -   7. Security        -   a. Shape detection    -   8. Inspection        -   a. Welding Industry        -   b. Plumbing        -   c. Bridge (Structural)        -   d. Nuclear        -   e. Oil Industry

While the invention has been discussed in terms of certain embodiments,it should be appreciated that the invention is not so limited. Theembodiments are explained herein by way of example, and there arenumerous modifications, variations and other embodiments that may beemployed that would still be within the scope of the embodimentsdisclosed herein.

Although the embodiments of the inventions have been disclosed in thecontext of certain embodiments and examples, it will be understood bythose skilled in the art that the present inventions extend beyond thespecifically disclosed embodiments to other alternative embodimentsand/or uses of the inventions and obvious modifications and equivalentsthereof. In addition, while a number of variations of the inventionshave been shown and described in detail, other modifications, which arewithin the scope of the inventions, will be readily apparent to those ofskill in the art based upon this disclosure. It is also contemplatedthat various combinations or subcombinations of the specific featuresand aspects of the embodiments may be made and still fall within one ormore of the inventions. Accordingly, it should be understood thatvarious features and aspects of the disclosed embodiments can be combinewith or substituted for one another in order to form varying modes ofthe disclosed inventions. For all of the embodiments described hereinthe steps of the methods need not be performed sequentially. Thus, it isintended that the scope of the present inventions herein disclosedshould not be limited by the particular disclosed embodiments describedabove.

What is claimed is:
 1. A computer-implemented method for evaluating astent in situ that when executed by a processor, performs the methodcomprising: selecting a region of interest in at least one medicalimage, wherein the region of interest comprises the stent in situ;identifying a background in the region of interest of the at least onemedical image; subtracting pixels from the at least one medical image,wherein the subtracted pixels represent the background; adjustingcontrast of the at least one medical image to produce a first enhancedimage; and outputting the first enhanced image to a user.
 2. Thecomputer-implemented method of claim 1, further comprising: measuringintensity values of different pixels in the first enhanced image; andcalculating a quantitative characteristic of the first enhanced imagebased on the measured intensity values.
 3. The computer-implementedmethod of claim 1, further comprising: identifying a first vertex of afirst cell in the stent within the first enhanced image; identifying asecond vertex of the first cell in the stent within the first enhancedimage; determining a distance between the first and the second vertexes,wherein the distance is correlated to stent elongation; accessing acomputer accessible index to determine whether the distance exceeds athreshold value for stent elongation; and outputting a recommendation tothe user related to the stent if the distance exceeds the thresholdvalue.
 4. The computer-implemented method of claim 1, furthercomprising: identifying an angle formed within a cell in the stentwithin the first enhanced image; determining a number of degrees in theangle; accessing a computer accessible index to determine whether thenumber of degrees exceeds a threshold value for stent elongation; andoutputting a recommendation to the user related to the stent if thenumber of degrees exceeds the threshold value.
 5. Thecomputer-implemented method of claim 1, further comprising: analyzing aline profile of the first enhanced image, wherein the line profile istaken across the width of the stent; determining the metal coverage ofthe line profile; accessing a computer accessible index to determinewhether the metal coverage is below a threshold value correlated to astent characteristic; and outputting a recommendation to the userrelated to the stent if the metal coverage is below the threshold value.6. The computer-implemented method of claim 1, further comprising:obtaining a second medical image, wherein the second medical imagecomprises an image of the stent in situ at a different time; processingthe second medical image to produce a second enhanced medical image;determining one or more disparities between the second enhanced medicalimage and the first enhanced image; accessing a computer accessibleindex to determine whether the one or more disparities exceed athreshold value; and outputting to the user a recommendation related tothe stent if the one or more disparities exceed the threshold value. 7.A system for enhancing a medical image comprising at least a processorand a memory, operating to serve as: an image processing moduleconfigured to select a region of interest in the medical image; an imagebackground subtraction module configured to identify and subtractbackground pixels of the medical image; a contrast adjustment moduleconfigured to adjust contrast of the medical image to generate a firstenhanced image; and an output module configured to output the firstenhanced image to a user.
 8. The system of claim 7, wherein theprocessor and the memory further operate to serve as: a vertex locatingmodule configured to identify a first vertex of a first cell in thestent within the first enhanced image, and identify a second vertex ofthe first cell in the stent within the first enhanced image; a distanceanalysis module configured to determine a distance between the first andthe second vertexes, wherein the distance represents stent elongation;an assessment module configured to access a computer accessible index todetermine whether the distance exceeds a threshold value for stentelongation; and wherein the output module is further configured tooutput a recommendation to the user related to the stent if the distanceexceeds the threshold value.
 9. The system of claim 7, wherein theprocessor and the memory further operate to serve as: an angle locatingmodule configured to identify an angle formed within a cell in the stentwithin the first enhanced image; an angle analysis module configured todetermine a number of degrees in the angle; an assessment moduleconfigured to access a computer accessible index to determine whetherthe number of degrees exceeds a threshold value for stent elongation;and wherein the output module is further configured to output arecommendation to the user related to the stent if the number of degreesexceeds the threshold value.
 10. The system of claim 7, wherein theprocessor and the memory further operate to serve as: a line profileanalysis module configured to determine the metal coverage of a lineprofile of the first enhanced image, wherein the line profile is takenacross the width of the stent; an assessment module configured to accessa computer accessible index to determine whether the metal coverage isbelow a threshold value for stent elongation; and wherein the outputmodule is configured to output a recommendation to the user related tothe stent if the metal coverage is below the threshold value.
 11. Thesystem of claim 7, wherein the processor and the memory further operateto serve as: a data retrieval module configured to obtain a secondmedical image of the stent in situ at a different time; wherein theimage processing module is further configured to generate a secondenhanced image from the second medical image; an image comparison moduleconfigured to determine one or more disparities between the secondenhanced image and the first enhanced image; and wherein the outputmodule is further configured to output the one or more disparities tothe user.